Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Satellite video is an emerging data source for dynamic Earth observation, which provides us
with a new means for large-scale moving vehicle detection and traffic monitoring. However …

Robust principal component analysis (RPCA) is a well-studied problem whose goal is to
decompose a matrix into the sum of low-rank and sparse components. In this paper, we …

In tracking of time-varying low-rank models of time-varying matrices, we present a method
robust to both uniformly-distributed measurement noise and arbitrarily-distributed “sparse” …

We primarily study a special a weighted low-rank approximation of matrices and then apply
it to solve the background modeling problem. We propose two algorithms for this purpose …

Weighted low rank approximation (WLRA) is an important yet computationally challenging
primitive with applications ranging from statistical analysis, model compression, and signal …

We propose a surprisingly simple model to estimate supervised video backgrounds. Our
model is based on L1 regression. As existing methods for L1 regression do not scale to high …

Given two disjoint sets, the best pair problem aims to find a point in one set and another
point in the other set with minimal distance between them. In this paper, we formulate the …

Singular value thresholding (SVT) plays an important role in the well-known robust principal
component analysis (RPCA) algorithms which have many applications in machine learning …

In this thesis, we study the problem of detecting moving objects from an image sequence
using low-rank and sparse representation concepts. The identification of changing or …